it seems that
EarthQuakes_in_Greece <- read.csv("EarthQuakes in Greece.csv")
earthquakes=EarthQuakes_in_Greece[,c(1,2,3,4,5,6,7,8)]
colnames(earthquakes)[6] <- "Lat"
colnames(earthquakes)[7] <- "Lon"
colnames(earthquakes)[8] <- "Richter"
str(earthquakes)
## 'data.frame': 256655 obs. of 8 variables:
## $ Year : int 1901 1901 1901 1902 1902 1902 1902 1903 1903 1903 ...
## $ Month : int 9 10 12 4 7 8 11 3 3 5 ...
## $ Date : int 12 25 24 11 5 2 5 15 25 29 ...
## $ Hours : int 6 16 23 18 14 5 23 19 22 9 ...
## $ Minutes: int 15 18 18 35 56 38 50 3 30 34 ...
## $ Lat : num 39 37 37.2 38.5 40.8 38.5 38.2 37.8 36 39.8 ...
## $ Lon : num 22.2 22.2 22.2 23.5 23.2 21.8 20.5 21.2 25 18.7 ...
## $ Richter: num 5.6 5.4 5.8 5.8 6.6 5.6 5.5 5.7 5.5 6 ...
earthquakes$DateFormatted <- as.Date(paste(earthquakes$Month, earthquakes$Date , earthquakes$Year), "%m %d %Y")
newmap <- getMap(resolution = "low")
plot(newmap, xlim = c(18, 30), ylim = c(33, 42), asp = 1)
points(earthquakes$Lon, earthquakes$Lat, col = "red", cex = .6)
sumYear <- earthquakes %>%
select(Year) %>%
group_by(Year) %>%
summarize(count = n())
datatable(sumYear)
ggplot(sumYear, aes(x = Year, y = count, colour = "yellow" , alpha = 1)) +
geom_point(colour = "blue") + geom_line(colour = "black")
ggplot(earthquakes, aes(x = Richter,
fill = "red",
alpha = 0.5)) +
geom_density(colour="blue")
years <- unique(earthquakes$Year)
averageRichter <- 1:length(years)
for (i in years){
temporary <- earthquakes %>% filter(Year==i)
averageRichter[years==i] <- mean(temporary$Richter)
}
averageRichterPerYear<- data.frame('year' = years, 'averageRichter' = averageRichter)
averageRichterPerYear
## year averageRichter
## 1 1901 5.600000
## 2 1902 5.875000
## 3 1903 6.114286
## 4 1904 5.966667
## 5 1905 5.853846
## 6 1906 5.600000
## 7 1907 6.200000
## 8 1908 5.566667
## 9 1909 5.737500
## 10 1910 5.600000
## 11 1911 5.630000
## 12 1912 5.626667
## 13 1913 5.500000
## 14 1914 5.483333
## 15 1915 5.754545
## 16 1916 5.460000
## 17 1917 5.344444
## 18 1918 5.238462
## 19 1919 5.346154
## 20 1920 5.250000
## 21 1921 5.285714
## 22 1922 5.442857
## 23 1923 5.230769
## 24 1924 5.133333
## 25 1925 5.207143
## 26 1926 5.608696
## 27 1927 5.385714
## 28 1928 5.446429
## 29 1929 5.050000
## 30 1930 5.607692
## 31 1931 5.407692
## 32 1932 5.472222
## 33 1933 5.364286
## 34 1934 5.450000
## 35 1935 5.781818
## 36 1936 5.166667
## 37 1937 5.225000
## 38 1938 5.392308
## 39 1939 5.350000
## 40 1940 5.550000
## 41 1941 5.350000
## 42 1942 5.400000
## 43 1943 5.273333
## 44 1944 5.654545
## 45 1945 5.340000
## 46 1946 5.471429
## 47 1947 5.453846
## 48 1948 5.517857
## 49 1949 5.436364
## 50 1950 5.012500
## 51 1951 5.208333
## 52 1952 5.065625
## 53 1953 5.040800
## 54 1954 4.918033
## 55 1955 4.966667
## 56 1956 5.000000
## 57 1957 5.041509
## 58 1958 4.892500
## 59 1959 4.885714
## 60 1960 4.865957
## 61 1961 4.866667
## 62 1962 4.968000
## 63 1963 4.797500
## 64 1964 4.968000
## 65 1965 4.043956
## 66 1966 3.955789
## 67 1967 3.678261
## 68 1968 3.622108
## 69 1969 3.716475
## 70 1970 3.543808
## 71 1971 3.514770
## 72 1972 3.525595
## 73 1973 3.470809
## 74 1974 3.360840
## 75 1975 3.369720
## 76 1976 3.441369
## 77 1977 3.382640
## 78 1978 3.620335
## 79 1979 3.617864
## 80 1980 3.454202
## 81 1981 3.615415
## 82 1982 3.386097
## 83 1983 3.557754
## 84 1984 3.430743
## 85 1985 3.438023
## 86 1986 3.355496
## 87 1987 3.467767
## 88 1988 3.519461
## 89 1989 3.339334
## 90 1990 3.258633
## 91 1991 3.247441
## 92 1992 3.336992
## 93 1993 3.326303
## 94 1994 3.446806
## 95 1995 3.178145
## 96 1996 3.407886
## 97 1997 3.344996
## 98 1998 3.308176
## 99 1999 3.261847
## 100 2000 3.295134
## 101 2001 3.328486
## 102 2002 3.283180
## 103 2003 3.306046
## 104 2004 3.117621
## 105 2005 3.157007
## 106 2006 3.200351
## 107 2007 3.228610
## 108 2008 3.096251
## 109 2009 2.928923
## 110 2010 2.825628
## 111 2011 1.881406
## 112 2012 1.725181
## 113 2013 1.830260
## 114 2014 1.897491
## 115 2015 1.887876
## 116 2016 1.928130
## 117 2017 2.057188
## 118 2018 1.993392
plt <- ggplot(averageRichterPerYear, aes(x = year, y = averageRichter)) +
geom_line(aes(group=1), colour="#000045")+ geom_point(size=1, colour="#CC0000") +
xlab("Year") + ylab("Average Richter") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("Earthquake Average Magnitude by year ")
plt
heatMap <- ggplot() + borders(colour="gray", fill="gray")+ geom_point() +xlim(18, 30) +ylim(33, 42)
heatMapCompleted <- heatMap + geom_density2d(data = earthquakes, aes(x=earthquakes$Lon, y=earthquakes$Lat, color=Richter), size = 0.1) +
stat_density2d(data = earthquakes,
aes(x=earthquakes$Lon, y=earthquakes$Lat, fill = ..level.., alpha = ..level..),
size = 0.1, bins = 15, geom = "polygon") + scale_fill_gradient(low = "yellow", high = "red") +
scale_alpha(range = c(0, 1), guide = FALSE) + xlab("Longtitude") + ylab("Latitude") +
ggtitle("Earthquake Positions in Greece (1901-2018) (Heat Map by frequency)") + coord_fixed(ratio = 1)
heatMapCompleted
## Warning: Removed 61 rows containing non-finite values (stat_density2d).
## Warning: Computation failed in `stat_density2d()`:
## cannot allocate vector of size 195.8 Mb
## Warning: Removed 61 rows containing non-finite values (stat_density2d).
heatMapPos <- ggplot(data = earthquakes) + borders(colour="gray", fill="gray") + xlim(18, 30) + ylim(33, 42)
heatmapPosCompleted <- heatMapPos + geom_point(aes(x=Lon, y=Lat, color=Richter), size=0.00001) + xlab("Longtitude") +
ylab("Latitude") + ggtitle("Earthquake Positions from 1965 to 2016") +
scale_colour_gradient(low = "darkkhaki", high = "darkmagenta") + coord_fixed(ratio = 1)
heatmapPosCompleted
## Warning: Removed 61 rows containing missing values (geom_point).
earthquakes %>%
leaflet() %>%
addTiles() %>%
addMarkers(lat=earthquakes$Lat, lng=earthquakes$Lon, clusterOptions = markerClusterOptions(),
popup= paste("<br><strong>Richter: </strong>", earthquakes$Richter,
"<br><strong>Date: </strong>", earthquakes$DateFormatted
))